1.先爬出CNN Politics 共54篇新聞內文，對文字做tf-idf處理
2.篩選出同時出現出現“Trump” & “GOP(共和黨)” 的新聞 ，做tf-idf處理，並顯現圖表
library(xml2)
library(rvest)
library(NLP)
library(tm)
library(tmcn)## # tmcn Version: 0.2-12library(htmltools)
library(readr)##
## Attaching package: 'readr'## The following object is masked from 'package:rvest':
##
## guess_encodinglibrary(tidytext)
library(wordcloud)## Loading required package: RColorBrewerlibrary(RColorBrewer)
library(Matrix)
library(rvest)
library(stringr)
#將CNN"politics"版第一頁每篇新聞html抓下來，存成txt檔
k <- readLines("file:///C:/Users/user/Desktop/NTU_COOL/week_5/news.txt" )## Warning in readLines("file:///C:/Users/user/Desktop/NTU_COOL/week_5/
## news.txt"): 於 'C:/Users/user/Desktop/NTU_COOL/week_5/news.txt' 找到不完整
## 的最後一列urlList <- list()
for(i in 2:99){
url <- k[i] %>% as.character()
urlList <- rbind(urlList, url)
}
urlList = unique(urlList)讀取新聞內文，一次讀太多會被網站封鎖，所以設停頓3秒
news.doc <- list()
for(url in urlList[2:55])
{
print(url)
html <- read_html(url) %>% html_nodes(".zn-body__paragraph") %>% html_text()
texts = ""
for (text in html)
{
texts = paste(texts, text)
}
news.doc <- rbind( news.doc, as.matrix(texts, sep=''))
print("sleeping...")
Sys.sleep(3)
print("Im awake")
}## [1] "https://edition.cnn.com/2018/10/16/politics/trump-pompeo-khashoggi-lawmakers/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/16/politics/trump-twitter-russia-horseface-pocahontas/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/16/politics/election-2018-democrats-good-bad-unknown/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/16/politics/michael-cohen-campaign-help-democrats/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/16/politics/biden-age-legitimate-issue/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/16/politics/federal-deficits-gop/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/16/politics/jeff-sessions-donald-trump/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/16/politics/orourke-republican-mom/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/16/politics/lindsey-graham-orrin-hatch-warren-trump/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/16/politics/trump-doubles-campaign-spending/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/16/politics/donald-trump-stormy-daniels/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/16/politics/elizabeth-warren-donald-trump-pocahontas/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/16/politics/devos-borrower-defense-lawsuit/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/08/18/politics/who-trump-attacks-insults-on-twitter/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/16/americas/migrant-caravan/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/15/politics/trump-painting-the-republican-club-presidents-white-house/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/16/politics/us-sanctions-iran-paramilitary/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/13/politics/white-house-ninth-circuit-judges/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/16/politics/donald-trump-fundraising/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/15/politics/president-trump-fundraising-surpasses-one-hundred-million/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/16/politics/donald-trump-stormy-daniels-horseface/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/16/politics/trump-warren-tweet-dna-test/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/16/politics/donald-trump-taxes/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/15/politics/trump-climate-change/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/16/politics/melania-trump-ti-video/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/13/politics/jared-kushner-taxes/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/13/politics/donald-trump-jr-jamal-khashoggi/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/16/politics/khashoggi-saudi-arabia-congress/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/16/politics/elizabeth-warren-donald-trump-2020-election/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/16/politics/rubio-khashoggi-arms-deal/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/15/politics/democrats-protest-grassley-judicial-nominee-recess/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/11/politics/trump-lawyers-preparing-answers-mueller-questions/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/09/26/politics/raskins-mueller-investigation/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/16/politics/pat-cipollone-white-house-counsel-trump/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/16/politics/cnn-poll-texas-cruz-orourke/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/16/politics/jim-jordan-leadership-race/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/13/politics/state-department-basra-consulate-security-funding/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/05/health/insects-gene-editing-crops-darpa/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/16/middleeast/khashoggi-saudi-pompeo-intl/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/15/politics/jim-mattis-trump-response-sort-of-a-democrat/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/13/politics/mnuchin-saudi-arabia-investor-conference/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/16/politics/the-forecast-democrats-have-a-problem-with-latino-voters/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/16/politics/democratic-fundraising-sweeps-republicans-in-key-house-races/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/15/opinions/how-the-saudis-played-trump-bergen/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/12/opinions/khashoggi-disappearance-trump-wrong-side-opinion-andelman/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/12/opinions/michelle-obama-is-still-right-kohn/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/11/opinions/trump-still-fascinated-by-his-rallies-even-if-fox-news-viewers-arent-dantonio/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/11/opinions/international-day-of-the-girl-michelle-obama/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/11/politics/democrats-republicans-personal-health-care-message/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/10/politics/donald-trump-medicare-for-all-midterms/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/11/politics/obamacare-premiums-2019/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/09/19/politics/trump-organization-midterm-democrats-investigations/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/08/31/politics/trump-foundation-new-york-attorney-general-lawsuit/index.html"
## [1] "sleeping..."
## [1] "Im awake"
## [1] "https://edition.cnn.com/2018/10/11/politics/trump-economy-trade-taxes/index.html"
## [1] "sleeping..."
## [1] "Im awake"清理文字
clean <- as.matrix(news.doc)
clean <- as.character(clean)
clean <- Corpus(VectorSource(clean))
cleanc <-{
cnn.space <-content_transformer(function(x , pattern ) gsub(pattern, " ", x))
clean <- tm_map(clean, removePunctuation )
clean <- tm_map(clean, removeWords, stopwords("english") )
clean <- tm_map(clean, stripWhitespace )
clean <- tm_map(clean, removeNumbers )
clean <- tm_map (clean, stemDocument)
}## Warning in tm_map.SimpleCorpus(clean, removePunctuation): transformation
## drops documents## Warning in tm_map.SimpleCorpus(clean, removeWords, stopwords("english")):
## transformation drops documents## Warning in tm_map.SimpleCorpus(clean, stripWhitespace): transformation
## drops documents## Warning in tm_map.SimpleCorpus(clean, removeNumbers): transformation drops
## documents## Warning in tm_map.SimpleCorpus(clean, stemDocument): transformation drops
## documentstd-idf
d.corpus <- Corpus(VectorSource(clean))
tdm <- TermDocumentMatrix(clean)
inspect(tdm)## <<TermDocumentMatrix (terms: 4387, documents: 54)>>
## Non-/sparse entries: 15121/221777
## Sparsity : 94%
## Maximal term length: 42
## Weighting : term frequency (tf)
## Sample :
## Docs
## Terms 1 15 29 3 33 37 41 44 45 49
## democrat 2 0 13 52 1 0 0 1 1 13
## hous 1 0 4 16 4 3 0 4 1 3
## presid 4 5 12 1 37 3 18 10 9 1
## republican 4 1 3 11 0 0 0 0 1 20
## said 16 9 9 0 15 10 7 2 1 10
## saudi 32 0 0 0 0 0 7 45 19 0
## state 2 9 0 10 2 17 2 17 6 0
## the 13 8 5 15 14 4 6 10 7 4
## trump 13 5 21 21 35 5 9 38 15 1
## will 5 5 9 11 3 0 2 3 3 0#tf
tf <- (as.matrix(tdm))
#idf
idf <- log( ncol(tf) / ( 1 + rowSums(tf != 0) ) )
tf <- apply(tdm, 2, sum) # term frequency
idf <- function(word_doc){ log2( (length(word_doc)+1) / nnzero(word_doc) ) }
idf <- apply(tdm, 1, idf)
doc.tfidf <- as.matrix(tdm)
for(i in 1:nrow(tdm)){
for(j in 1:ncol(tdm)){
doc.tfidf[i,j] <- (doc.tfidf[i,j] / tf[j]) * idf[i]
}
}
doc.tfidf## Docs
## Terms 1 2
## abduct 0.0097364204 0.0000000000
## absolv 0.0067069138 0.0000000000
## account 0.0137401726 0.0000000000
## acknowledg 0.0034501216 0.0000000000
## action 0.0048281206 0.0000000000
## actual 0.0026936521 0.0000000000
## administr 0.0106735420 0.0030791811
## advic 0.0048682102 0.0000000000
## advis 0.0076440738 0.0044104362
## afternoon 0.0043867282 0.0000000000
## alleg 0.0037388276 0.0032358127
## alreadi 0.0041330852 0.0000000000
## amateur 0.0067069138 0.0000000000
## and 0.0010712050 0.0000000000
## anoth 0.0041330852 0.0000000000
## answer 0.0053873042 0.0000000000
## appar 0.0032266354 0.0000000000
## appear 0.0065236744 0.0000000000
## approach 0.0043867282 0.0000000000
## arabia 0.0427975340 0.0098772354
## argu 0.0028531689 0.0000000000
## arm 0.0085595068 0.0000000000
## around 0.0022900288 0.0000000000
## asked 0.0032266354 0.0055850597
## assault 0.0043867282 0.0000000000
## assembl 0.0067069138 0.0000000000
## assess 0.0043867282 0.0000000000
## associat 0.0040132617 0.0000000000
## assum 0.0055468210 0.0000000000
## author 0.0025480246 0.0000000000
## authoritarian 0.0067069138 0.0000000000
## back 0.0012539675 0.0043410442
## bad 0.0026936521 0.0046625062
## barbara 0.0048682102 0.0000000000
## barrett 0.0048682102 0.0000000000
## began 0.0048281206 0.0083571084
## believ 0.0016930761 0.0029305856
## ben 0.0055468210 0.0000000000
## bergen 0.0055468210 0.0000000000
## bilater 0.0067069138 0.0000000000
## bin 0.0037081174 0.0000000000
## bipartisan 0.0048682102 0.0000000000
## blame 0.0055468210 0.0000000000
## bodi 0.0043867282 0.0000000000
## bottom 0.0034501216 0.0000000000
## brett 0.0040132617 0.0000000000
## buy 0.0043867282 0.0000000000
## buyer 0.0067069138 0.0000000000
## call 0.0050158700 0.0021705221
## campaign 0.0012539675 0.0000000000
## can 0.0010712050 0.0037083483
## candid 0.0033861522 0.0000000000
## cardin 0.0067069138 0.0000000000
## care 0.0021745581 0.0000000000
## carlo 0.0043867282 0.0000000000
## carolina 0.0043867282 0.0000000000
## case 0.0080809562 0.0000000000
## chamber 0.0040132617 0.0000000000
## chang 0.0024140603 0.0041785542
## claim 0.0018694138 0.0097074380
## clarissa 0.0055468210 0.0000000000
## classifi 0.0055468210 0.0000000000
## clear 0.0017789237 0.0030791811
## cnn 0.0085696401 0.0000000000
## cnnpresid 0.0032266354 0.0000000000
## cnns 0.0030671185 0.0000000000
## come 0.0011299360 0.0000000000
## commit 0.0074162348 0.0000000000
## committe 0.0021745581 0.0000000000
## compar 0.0048682102 0.0000000000
## compat 0.0055468210 0.0000000000
## complain 0.0048682102 0.0000000000
## complet 0.0030295066 0.0052438448
## concern 0.0080809562 0.0000000000
## concert 0.0067069138 0.0000000000
## confirm 0.0032266354 0.0055850597
## confront 0.0043867282 0.0000000000
## congress 0.0017789237 0.0000000000
## consequenti 0.0134138276 0.0000000000
## consul 0.0199721825 0.0000000000
## continu 0.0022598719 0.0019558329
## contractor 0.0048682102 0.0000000000
## contribut 0.0014591621 0.0000000000
## convers 0.0074162348 0.0064184683
## countri 0.0042848200 0.0000000000
## cours 0.0037081174 0.0000000000
## court 0.0028531689 0.0000000000
## cover 0.0043867282 0.0000000000
## creat 0.0025480246 0.0000000000
## credibl 0.0240795704 0.0000000000
## crisi 0.0111243522 0.0128369366
## crown 0.0276009725 0.0000000000
## curbelo 0.0055468210 0.0000000000
## cut 0.0024140603 0.0000000000
## dark 0.0110936420 0.0000000000
## date 0.0040132617 0.0000000000
## day 0.0041637954 0.0000000000
## deal 0.0020665426 0.0000000000
## death 0.0064532708 0.0000000000
## decis 0.0016114180 0.0027892415
## declin 0.0034501216 0.0000000000
## defens 0.0022900288 0.0000000000
## democrat 0.0014186420 0.0012277805
## deni 0.0048281206 0.0000000000
## describ 0.0022900288 0.0000000000
## desert 0.0055468210 0.0000000000
## design 0.0037081174 0.0000000000
## determin 0.0028531689 0.0000000000
## diamond 0.0067069138 0.0000000000
## die 0.0074162348 0.0000000000
## difficult 0.0040132617 0.0000000000
## diplomat 0.0111243522 0.0064184683
## direct 0.0078603103 0.0034013993
## disappear 0.0181770398 0.0000000000
## divert 0.0067069138 0.0000000000
## doesnt 0.0045800575 0.0000000000
## donald 0.0005329833 0.0018451069
## done 0.0032266354 0.0055850597
## dont 0.0027758636 0.0048048081
## doubt 0.0043867282 0.0000000000
## downplay 0.0055468210 0.0000000000
## dure 0.0034501216 0.0000000000
## echo 0.0080265235 0.0000000000
## element 0.0055468210 0.0000000000
## elimin 0.0043867282 0.0000000000
## elise 0.0067069138 0.0000000000
## emerg 0.0037081174 0.0000000000
## end 0.0016930761 0.0029305856
## enough 0.0028531689 0.0000000000
## ensur 0.0032266354 0.0000000000
## episod 0.0048682102 0.0000000000
## essenti 0.0043867282 0.0000000000
## even 0.0011908030 0.0020611892
## ever 0.0034501216 0.0000000000
## evid 0.0024140603 0.0000000000
## expand 0.0043867282 0.0000000000
## explan 0.0110936420 0.0000000000
## extens 0.0032266354 0.0000000000
## fact 0.0018694138 0.0032358127
## facto 0.0048682102 0.0000000000
## familiar 0.0080809562 0.0046625062
## far 0.0020665426 0.0000000000
## fashion 0.0067069138 0.0000000000
## father 0.0034501216 0.0000000000
## figur 0.0034501216 0.0000000000
## find 0.0030295066 0.0000000000
## float 0.0048682102 0.0000000000
## florida 0.0085595068 0.0000000000
## foreign 0.0072421810 0.0000000000
## forthcom 0.0097364204 0.0000000000
## found 0.0028531689 0.0000000000
## four 0.0028531689 0.0000000000
## fox 0.0064532708 0.0111701193
## fractious 0.0067069138 0.0000000000
## fret 0.0067069138 0.0000000000
## full 0.0030295066 0.0000000000
## global 0.0030295066 0.0000000000
## got 0.0069002431 0.0000000000
## govern 0.0020665426 0.0000000000
## graham 0.0146046307 0.0000000000
## ground 0.0048682102 0.0000000000
## grow 0.0030295066 0.0000000000
## guilti 0.0097364204 0.0000000000
## guy 0.0080265235 0.0000000000
## halt 0.0067069138 0.0000000000
## handl 0.0040132617 0.0000000000
## happen 0.0107746083 0.0000000000
## hard 0.0028531689 0.0000000000
## heir 0.0055468210 0.0000000000
## held 0.0069002431 0.0000000000
## hell 0.0040132617 0.0000000000
## here 0.0040132617 0.0000000000
## hesit 0.0055468210 0.0000000000
## high 0.0020665426 0.0000000000
## highrank 0.0055468210 0.0000000000
## hike 0.0048682102 0.0000000000
## hold 0.0037388276 0.0000000000
## hour 0.0032266354 0.0000000000
## hous 0.0010712050 0.0018541741
## human 0.0107746083 0.0000000000
## identifi 0.0025480246 0.0000000000
## ignor 0.0040132617 0.0000000000
## impact 0.0096799062 0.0000000000
## inact 0.0067069138 0.0000000000
## includ 0.0022598719 0.0078233317
## increas 0.0026936521 0.0000000000
## individu 0.0021745581 0.0000000000
## inform 0.0034501216 0.0000000000
## initi 0.0034501216 0.0000000000
## inject 0.0067069138 0.0000000000
## innoc 0.0201207415 0.0000000000
## insid 0.0090885199 0.0000000000
## insul 0.0048682102 0.0000000000
## intellig 0.0175469128 0.0075930918
## interoper 0.0067069138 0.0000000000
## interrog 0.0120397852 0.0000000000
## interview 0.0015335593 0.0000000000
## introduc 0.0043867282 0.0000000000
## investig 0.0033861522 0.0029305856
## iran 0.0064532708 0.0000000000
## iranian 0.0037081174 0.0000000000
## isnt 0.0025480246 0.0000000000
## issu 0.0025079350 0.0000000000
## its 0.0013196097 0.0000000000
## jake 0.0055468210 0.0000000000
## jamal 0.0028531689 0.0000000000
## jeremi 0.0048682102 0.0000000000
## journalist 0.0080809562 0.0000000000
## just 0.0042848200 0.0018541741
## justic 0.0057063379 0.0098772354
## kavanaugh 0.0087734564 0.0000000000
## keep 0.0025480246 0.0000000000
## khashoggi 0.0485038718 0.0000000000
## kill 0.0064532708 0.0000000000
## kind 0.0028531689 0.0000000000
## king 0.0032266354 0.0055850597
## kingdom 0.0120397852 0.0000000000
## know 0.0037388276 0.0097074380
## knowledg 0.0151475331 0.0000000000
## known 0.0028531689 0.0000000000
## labott 0.0067069138 0.0000000000
## largest 0.0040132617 0.0000000000
## last 0.0022598719 0.0019558329
## later 0.0037388276 0.0064716253
## launch 0.0040132617 0.0000000000
## lawmak 0.0111243522 0.0000000000
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## Docs
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## Docs
## Terms 33 34
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## Docs
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## [ reached getOption("max.print") -- omitted 2536 rows ]篩選出同時有“Trump” & “GOP(共和黨)”的文章，將tf-idf做rowsum並做出圖表
cleanText = c()
for (t in clean)
{
cleanText <- append(cleanText,t)
}
cleanText$language = NULL
GOPTexts = c()
for (count in str_count(cleanText, "Trump"))
{
GOPTexts = append(GOPTexts, count != 0)
}
l = str_count(cleanText, "GOP")
for (i in 1:54)
{
GOPTexts[i] = GOPTexts[i] || l[i]
}
newTFIDF = as.data.frame(doc.tfidf)
select <- newTFIDF[,GOPTexts]
sums = rowSums(x=select)
selectr <-as.data.frame(sums)
selectr <- as.data.frame(selectr)
colnames(selectr)## [1] "sums"selectr## sums
## abduct 0.023418981
## absolv 0.006706914
## account 0.133039806
## acknowledg 0.046101921
## action 0.071399741
## actual 0.058704103
## administr 0.225010580
## advic 0.030987539
## advis 0.064232955
## afternoon 0.020342750
## alleg 0.105741771
## alreadi 0.050854387
## amateur 0.006706914
## and 0.085944953
## anoth 0.057138971
## answer 0.100266395
## appar 0.032168988
## appear 0.103867770
## approach 0.026051740
## arabia 0.262065611
## argu 0.071961644
## arm 0.075969116
## around 0.054505923
## asked 0.052703594
## assault 0.020159245
## assembl 0.006706914
## assess 0.026193550
## associat 0.053365025
## assum 0.020774081
## author 0.059306266
## authoritarian 0.006706914
## back 0.068319174
## bad 0.075098792
## barbara 0.041321762
## barrett 0.021854453
## began 0.060484271
## believ 0.082954766
## ben 0.011449734
## bergen 0.009686526
## bilater 0.006706914
## bin 0.094036324
## bipartisan 0.020562285
## blame 0.016438301
## bodi 0.031662124
## bottom 0.038581382
## brett 0.026649257
## buy 0.036134030
## buyer 0.006706914
## call 0.103438638
## campaign 0.244227580
## can 0.090341640
## candid 0.167516096
## cardin 0.006706914
## care 0.183072529
## carlo 0.030169939
## carolina 0.035670126
## case 0.071362568
## chamber 0.024912154
## chang 0.144642401
## claim 0.165197742
## clarissa 0.016438301
## classifi 0.005546821
## clear 0.080538993
## cnn 0.122974045
## cnnpresid 0.064277532
## cnns 0.095611777
## come 0.078836095
## commit 0.039008859
## committe 0.113367138
## compar 0.024537010
## compat 0.016438301
## complain 0.024217045
## complet 0.053225772
## concern 0.059288638
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## usmca 0.033036341遇到問題，一直無法將selectr跟Term轉成dataframe